Spiking Neural Network

Spiking neural networks (SNNs), inspired by the brain's event-driven communication, aim to create energy-efficient artificial intelligence by processing information through binary spikes rather than continuous values. Current research emphasizes improving training efficiency through novel neuron models (e.g., parallel resonate and fire neurons, multi-compartment neurons), developing specialized weight initialization methods, and exploring various coding schemes (e.g., Poisson coding, stepwise weighted spike coding) to optimize performance and reduce energy consumption. This field is significant due to SNNs' potential for low-power applications in embedded systems, neuromorphic computing, and real-time signal processing tasks like robotic manipulation and brain-computer interfaces.

Papers

June 30, 2023

Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings
Yuan Zhang, Jian Cao, Ling Zhang, Jue Chen, Wenyu Sun, Yuan Wang
Spiking Neural Network Event Data Temporal Embeddings Dynamic Vision Sensor Global Temporal

June 27, 2023

S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
Marco Paul E. Apolinario, Kaushik Roy
Neural Network Spiking Neural Network Back Propagation Spike Timing Dependent Plasticity Local Learning Rule

June 22, 2023

Accelerating SNN Training with Stochastic Parallelizable Spiking Neurons
Sidi Yaya Arnaud Yarga, Sean U. N. Wood
Spiking Neural Network Neuromorphic Hardware Leaky Integrate and Fire Leaky Integrate and Fire Neuron Non Spiking

June 21, 2023

June 14, 2023

High-performance deep spiking neural networks with 0.3 spikes per neuron
Ana Stanojevic, Stanisław Woźniak, Guillaume Bellec, Giovanni Cherubini, Angeliki Pantazi, Wulfram Gerstner
Neural Network Spiking Neural Network Single Neuron Level Spike Protein High Performance Binary Spike Spike Timing

June 11, 2023

An Efficient and Accurate Memristive Memory for Array-based Spiking Neural Networks
Hritom Das, Rocco D. Febbo, SNB Tushar, Nishith N. Chakraborty, Maximilian Liehr, Nathaniel Cady, Garrett S. Rose
High Efficiency Spiking Neural Network Neuromorphic Computing Memristive Device Neuromorphic Computing Application Memristive Synapsis

June 9, 2023

Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training
Edoardo W. Grappolini, Anand Subramoney
Neural Network Deep Learning Spiking Neural Network Back Propagation Balancing Weight Synaptic Weight Synaptic Delay

June 7, 2023

Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP
Arsham Gholamzadeh Khoee, Alireza Javaheri, Saeed Reza Kheradpisheh, Mohammad Ganjtabesh
Catastrophic Forgetting Spiking Neural Network Neuromorphic Hardware Spike Based Neuromorphic

June 1, 2023

Auto-Spikformer: Spikformer Architecture Search
Kaiwei Che, Zhaokun Zhou, Zhengyu Ma, Wei Fang, Yanqi Chen, Shuaijie Shen, Li Yuan, Yonghong Tian
Neural Network Spiking Neural Network SNN Architecture Spikformer Architecture

May 31, 2023

May 30, 2023

May 27, 2023

Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing
Yuhang Li, Abhishek Moitra, Tamar Geller, Priyadarshini Panda
Neural Network Spiking Neural Network Memory Computing Single Neural Network Binary Spike Static Deep Neural Network

May 26, 2023

May 25, 2023

Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks
Gehua Ma, Rui Yan, Huajin Tang
LeArning Abstract Spiking Neural Network Computation Method New Resource Neural Computation Neuromorphic Artificial Intelligence Harnessing Inherent Noise

May 23, 2023

Spiking Neural Network

Papers

Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings

S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks

Accelerating SNN Training with Stochastic Parallelizable Spiking Neurons

NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN

Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection

High-performance deep spiking neural networks with 0.3 spikes per neuron

An Efficient and Accurate Memristive Memory for Array-based Spiking Neural Networks

Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training

Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP

Auto-Spikformer: Spikformer Architecture Search

Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN

Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA

A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks

Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function

Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing

Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks

A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks

Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks

Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency

Unsupervised Spiking Neural Network Model of Prefrontal Cortex to study Task Switching with Synaptic deficiency